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Safety-04 Calculation

Source: R/safety_04.R
safety_04.Rd

The safety_04 function processes EMS incident data for specific safety and transport criteria, filtering by patient age and incident type to identify cases that meet specified exclusion or inclusion criteria. This function accommodates data with various EMS-specific codes, age descriptors, and procedure identifiers.

Usage

safety_04(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  arrest_table = NULL,
  injury_table = NULL,
  procedures_table = NULL,
  disposition_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  eresponse_05_col,
  earrest_01_col,
  einjury_03_col,
  eprocedures_03_col,
  edisposition_14_col,
  transport_disposition_col,
  confidence_interval = FALSE,
  method = c("wilson", "clopper-pearson"),
  conf.level = 0.95,
  correct = TRUE,
  ...
)

Arguments

df

A dataframe or tibble contianing EMS data where each row represents an observation and columns represent features.

patient_scene_table

A data.frame or tibble containing at least ePatient, and eScene as a fact table.

response_table

A data.frame or tibble containing at least the eResponse fields needed for this measure's calculations.

arrest_table

A data.frame or tibble containing at least the eArrest fields needed for this measure's calculations.

injury_table

A data frame or tibble containing fields from eInjury needed for this measure's calculations.

procedures_table

A dataframe or tibble containing at least the eProcedures fields needed.

disposition_table

A data.frame or tibble containing only the edisposition fields needed for this measure's calculations.

erecord_01_col

The column representing the EMS record unique identifier.

incident_date_col

Column that contains the incident date. This defaults to NULL as it is optional in case not available due to PII restrictions.

patient_DOB_col

Column that contains the patient's date of birth. This defaults to NULL as it is optional in case not available due to PII restrictions.

epatient_15_col

Column representing the patient's numeric age agnostic of unit.

epatient_16_col

Column representing the patient's age unit ("Years", "Months", "Days", "Hours", or "Minutes").

eresponse_05_col

Column that contains eResponse.05 or the response type.

earrest_01_col

Column representing whether or not the patient is in arrest.

einjury_03_col

Column describing Trauma triage criteria for the red boxes (Injury Patterns and Mental Status and Vital Signs) in the 2021 ACS National Guideline for the Field Triage of Injured Patients.

eprocedures_03_col

Column containing procedure codes with or without procedure names.

edisposition_14_col

Column giving the position of the patient during transport from the scene.

transport_disposition_col

One or more unquoted column names (such as edisposition.12, edisposition.30) containing transport disposition for an EMS event identifying whether a transport occurred and by which unit.

confidence_interval

Logical. If TRUE, the function calculates a confidence interval for the proportion estimate.

method

Character. Specifies the method used to calculate confidence intervals. Options are "wilson" (Wilson score interval) and "clopper-pearson" (exact binomial interval). Partial matching is supported, so "w" and "c" can be used as shorthand.

conf.level

Numeric. The confidence level for the interval, expressed as a proportion (e.g., 0.95 for a 95% confidence interval). Defaults to 0.95.

correct

Logical. If TRUE, applies a continuity correction to the Wilson score interval when method = "wilson". Defaults to TRUE.

...

optional additional arguments to pass onto dplyr::summarize.

Value

A data.frame summarizing results for two population groups (All, Adults and Peds) with the following columns:

  • pop: Population type (All, Adults, and Peds).

  • numerator: Count of incidents meeting the measure.

  • denominator: Total count of included incidents.

  • prop: Proportion of incidents meeting the measure.

  • prop_label: Proportion formatted as a percentage with a specified number of decimal places.

  • lower_ci: Lower bound of the confidence interval for prop (if confidence_interval = TRUE).

  • upper_ci: Upper bound of the confidence interval for prop (if confidence_interval = TRUE).

Author

Nicolas Foss, Ed.D., MS

Examples


# Synthetic test data
  test_data <- tibble::tibble(
    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    epatient_15 = c(34, 5, 45, 2, 60),  # Ages
    epatient_16 = c("Years", "Years", "Years", "Months", "Years"),
    eresponse_05 = rep(2205001, 5),
    earrest_01 = rep("No", 5),
    einjury_03 = rep("non-injury", 5),
    edisposition_14 = rep(4214001, 5),
    edisposition_30 = rep(4230001, 5),
    eprocedures_03 = rep("other response", 5)
  )

# Run the function
# Return 95% confidence intervals using the Wilson method
  safety_04(
    df = test_data,
    erecord_01_col = erecord_01,
    incident_date_col = NULL,
    patient_DOB_col = NULL,
    epatient_15_col = epatient_15,
    epatient_16_col = epatient_16,
    eresponse_05_col = eresponse_05,
    earrest_01_col = earrest_01,
    einjury_03_col = einjury_03,
    edisposition_14_col = edisposition_14,
    transport_disposition_col = edisposition_30,
    eprocedures_03_col = eprocedures_03,
    confidence_interval = TRUE
  )
#> 
#> ── Safety-04 ───────────────────────────────────────────────────────────────────
#> 
#> ── Gathering Records for Safety-04 ──
#> 
#> Running `safety_04_population()`  [Working on 1 of 13 tasks] ●●●───────────────…
#> Running `safety_04_population()`  [Working on 2 of 13 tasks] ●●●●●●────────────…
#> Running `safety_04_population()`  [Working on 3 of 13 tasks] ●●●●●●●●──────────…
#> Running `safety_04_population()`  [Working on 4 of 13 tasks] ●●●●●●●●●●────────…
#> Running `safety_04_population()`  [Working on 5 of 13 tasks] ●●●●●●●●●●●●●─────…
#> Running `safety_04_population()`  [Working on 6 of 13 tasks] ●●●●●●●●●●●●●●●───…
#> Running `safety_04_population()`  [Working on 7 of 13 tasks] ●●●●●●●●●●●●●●●●●─…
#> Running `safety_04_population()`  [Working on 8 of 13 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `safety_04_population()`  [Working on 9 of 13 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `safety_04_population()`  [Working on 10 of 13 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `safety_04_population()`  [Working on 11 of 13 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `safety_04_population()`  [Working on 12 of 13 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `safety_04_population()`  [Working on 13 of 13 tasks] ●●●●●●●●●●●●●●●●●…
#> 
#> 
#> 
#> ── Calculating Safety-04 ──
#> 
#> 
#> ✔ Function completed in 0.21s.
#> 
#> Warning: In `prop.test()`: Chi-squared approximation may be incorrect for any n < 10.
#> # A tibble: 1 × 8
#>   measure   pop   numerator denominator  prop prop_label lower_ci upper_ci
#>   <chr>     <chr>     <int>       <int> <dbl> <chr>         <dbl>    <dbl>
#> 1 Safety-04 Peds          2           2     1 100%          0.198        1